To address the shortcomings of the autofocus-window construction technology used in conventional focusing methods for laser processing, such as limited real-time performance, vulnerability to background noise, and considerable computational complexity in microtexture applications, this study proposes a saliency-window construction method. This method utilizes image saliency to isolate the region of interest for focus evaluation by integrating gradients and color cues. The main idea is to apply a visual saliency-detection framework when generating an autofocus window to suppress irrelevant background information and enhance the specificity of sharpness evaluation. A window-extraction algorithm combining Kirsch gradient analysis, LAB color-contrast calculation, local normalization, and texture suppression is introduced, thus enabling microtexture regions to be reliably depicted under brightness variations and complex modes. The method was experimentally verified on a laser microprocessing device. Compared with the conventional central-window method, the proposed method exhibits higher levels of sensitivity, steepness, and sharpness ratio by approximately 83%, 75%, and 54%, respectively, thereby enabling the generation of single-peak and fast-response focus curves and supporting stable microstructure manufacturing. Owing to its ability to balance between high precision and real-time performance, this saliency window provides an effective solution for the laser microprocessing platform.